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Ali Selamat
Faculty of Computing, Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Machine Learning Approaches on External Plagiarism Detection Imam Much Ibnu Subroto; Ali Selamat; Badieah Assegaf
Journal of Telematics and Informatics Vol 4, No 2: September 2016
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (579.768 KB) | DOI: 10.12928/jti.v4i2.

Abstract

External plagiarism detection is a technique that refers to the comparison between suspicious document and different sources. External plagiarism models are generally preceded by candidate document retrieval and further analysis and then performed to determine the plagiarism occurring. Currently most of the external plagiarism detection is using similarity measurement approaches that are expressed by a pair of sentences or phrase considered similar. Similarity techniques approach is more easily understood using a formula which compares term or token between the two documents. In contrast to the approach of machine learning techniques which refer to the pattern matching and cannot directly comparing token or term between two documents. This paper proposes some machine learning techniques such as k-nearest neighbors (KNN), support vector machine (SVM) and artificial neural network (ANN) for external plagiarism detection and comparing the result with Cosine similarity measurement approach. This paper presented density based that normalized by frequency as the pattern. The result showed that all machine learning approach used in this experiment has better performance in term of accuracy, precision and recall.
Co-Authors Badieah Assegaf Imam Much Ibnu Subroto